This thesis explores the representation of probability measures in a coherent Bayesian modelling framework, together with the ensuing characterisation properties of posterior functionals. First, a decision theoretic approach is adopted to provide a unified modelling criterion applicable to assessing prior-likelihood combinations, design matrices, model dimensionality and choice of sample size. The utility structure and associated Bayes risk induces a distance measure, introducing concepts from differential geometry to aid in the interpretation of modelling characteristics. Secondly, analytical and approximate computations for the implementation of the Bayesian paradigm, based on the properties of the class of transformation models, are discussed. Finally, relationships between distance measures (in the form of either a derivative of a Bayes mapping or an induced distance) are explored, with particular reference to the construction of sensitivity measures.

This thesis re-examines the Bayes hierarchical linear model and the associated issue of variance component estimation in the light of new numerical procedures, and demonstrates that the Bayes linear model is indeed a practical proposition. Technical issues considered include the development of analytical procedures essential for efficient evaluation of the likelihood function, and a partial characterisation of the difficulty of likelihood evaluation. A general non-informative prior distribution for the hierarchical linear model is developed. Extensions to spherically symmetric error distributions are shown to be practicable and useful. The numerical technique enables the sensitivity of the results to the prior structure, error structure and model structure to be investigated. An extended example is considered which illustrates these analytical and numerical techniques in a 15 dimensional problem. A second example provides a critical examination of a British Standards Institute paper, and develops further techniques for handling alternative spherically symmetric error distributions. Recent work on variance component estimation is viewed from the Bayesian perspective, and areas for further work are identified.

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Collections of individual case safety reports are the main resource for early discovery of unknown adverse reactions to drugs once they have been introduced to the general public. The data sets involved are complex and based on voluntary submission of reports, but contain pieces of very important information. The aim of this thesis is to propose computationally feasible statistical methods for large-scale knowledge discovery in these data sets. The main contributions are a duplicate detection method that can reliably identify pairs of unexpectedly similar reports and a new measure for highlighting suspected drug-drug interaction. Specifically, we extend the hit-miss model for database record matching with a hit-miss mixture model for scoring numerical record fields and a new method to compensate for strong record field correlations. The extended hit-miss model is implemented for the WHO database and demonstrated to be useful in real world duplicate detection, despite the noisy and incomplete information on individual case safety reports. The Information Component measure of disproportionality has been in routine use since 1998 to screen the WHO database for excessive adverse drug reaction reporting rates. Here, it is further refined. We introduce improved credibility intervals for rare events, post-stratification adjustment for suspected confounders and an extension to higher order associations that allows for simple but robust screening for potential risk factors. A new approach to identifying reporting patterns indicative of drug-drug interaction is also proposed. Finally, we describe how imprecision estimates specific to each prediction of a Bayes classifier may be obtained with the Bayesian bootstrap. Such case-based imprecision estimates allow for better prediction when different types of errors have different associated loss, with a possible application in combining quantitative and clinical filters to highlight drug-ADR pairs for clinical review.

Thesis ( Ph.D.) -- McMaster University, 2002. / Includes bibliographical references. Also available via World Wide Web.

Thesis (M.S.)--Ohio University, June, 1984. / Title from PDF t.p.

Thesis (Ph. D.)--University of Adelaide, 1988. / Errate slip inserted. Includes bibliographical references (leaves 130-134).

Licentiatavhandling Linköping : Linköpings universitet, 2006.

Thesis (Ph.D.)--Bowling Green State University, 2009. / Document formatted into pages; contains viii, 108 p. Includes bibliographical references.

Thesis (Ph. D.)--Auburn University. / Abstract. Vita. Includes bibliographical references (p. 132-135).